Multi-Modal Pedestrian Trajectory Prediction for Edge Agents Based on Spatial-Temporal Graph

• Main Authors: Xiangyu Zou, Bin Sun, Duan Zhao, Zongwei Zhu, Jinjin Zhao, Yongxin He
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Trajectory prediction; spatial-temporal graph; generative adversarial network; global node; scaled dot product attention
• Online Access: https://ieeexplore.ieee.org/document/9082663/

Edge agents, represented by socially-aware robots and autonomous vehicles, have gradually been integrated into human society. The safety navigation system in interactive scenes is of great importance to them. The key of this system is that the edge agent has the ability to predict the pedestrian trajectory in the dynamic scene, so as to avoid collision. However, predicting pedestrian trajectories in dynamic scenes is not an easy task, because it is necessary to comprehensively consider the spatial-temporal structure of human-environment interaction, visual attention, and the multi-modal behavior of human walking. In this paper, a scalable spatial-temporal graph generation adversarial network architecture (STG-GAN) is introduced, which can comprehensively consider the influence of human-environment interaction and generate a reasonable multi-modal prediction trajectory. First, we use LSTM nodes to flexibly transform the spatial-temporal graph of human-environment interactions into feed-forward differentiable feature coding, and innovatively propose the global node to integrate scene context information. Then, we capture the relative importance of global interactions on pedestrian trajectories through scaled dot product attention, and use recurrent sequence modeling and generative adversarial network architecture for common training, so as to generate reasonable pedestrian future trajectory distributions based on rich mixed features. Experiments on public data sets show that STG-GAN is superior to previous work in terms of accuracy, reasoning speed and rationality of trajectory prediction.